There are numerous devices both implantable and external that have been used to monitor various medical patient conditions. Well known for heart patients is the Holter monitor which permits somewhat uncomfortable monitoring of an electrocardiogram for 24 hours which can then be read by a physician to find anamolies in the rhythm which were not susceptible to discovery or confirmation in a patient's office visit to the doctor. A number of other devices have improved on the ability to maintain records of electrocardiograms and numerous other health related patient parameters and even device performance parameters. Implantable medical devices such as pacemakers and cardioverter-defibrillators and even non-therapeutic monitoring devices are currently capable of maintaining some records and reporting out such data. An example of a non-therapy delivering monitoring implantable medical device can be seen in U.S. Pat. Nos. 5,313,953 and 5,411,031 issued to Yomtov et al., and in Holsbach et al, 5,312,446, and others. Nolan et al,'s U.S. Pat. No. 5,404,877 teaches that such devices can even generate patient alarms. All these patents are incorporated herein by this reference in that they provide information about what can currently be done in the implantable device field.
Current generation pacemakers and implantable defibrillators/cardioverters have the ability to store different types of information in order to provide feedback to the clinician about the patient/device system. Examples of stored information include arrhythmia diagnostics, histograms of paced and sensed events, electrograms and trends of lead impedance. Such information is useful not only in optimizing device programming but also in the management of the patient's arrhythmias and other conditions. While our invention focuses on the monitoring of patient activity, which we use as a functional status monitor, the additional information available from implantable devices could be used as an adjunct.
However, to date the literature is devoid of a satisfactory description of how to use activity information. There has been considerable thinking in this area, but none have yet succeeded in producing a satisfactory measure to track patient functional status. Some examples of this thinking in the current literature include:
Walsh J. T., Charlesworth A., Andrews R., Hawkins M., and Cowley A. J. "Relation of daily activity levels in patients with chronic heart failure to long-term prognosis", Am J Cardiol, 1997, 79: 1364-1369. PA1 Rankin S. L., Brifa T. G., Morton A. R., and Hung J., "A specific activity questionnaire to measure the functional capacity of cardiac patients", Am J Cardiol 1996, 77: 1220-1223. PA1 Davies S. W., Jordan S. L., and Lipkin D. P., "Use of limb movement sensors as indicators of the level of everyday physical activity in chronic congestive heart failure", Am J Cardiol 1992, 67: 1581-1586. PA1 Hoodless D. J., Stainer K., Savic N., Batin P., Hawkins M. and Cowley A. J., "Reduced customary activity in chronic heart failure: assessment with a new shoe-mounted pedometer", International Journal of Cardiology, 1994, 43: 39-42. PA1 Alt E., Matula M., Theres H., Heinz M. and Baker R., "The basis for activity controlled rate variable cardiac pacemakers: An analysis of mechanical forces on the human body induced by exercise and environment", PACE, vol 12, October 1989. PA1 Lau C. P., Mehta D., Toff W. D., Stott R. J., Ward D. E. and Camm A. J., "Limitations of rate response of an activity sensing rate responsive pacemaker to different forms of activity", PACE, vol. 11, February 1988, and PA1 Lau C. P., Stott J. R. R., Zetlin M. B., Ward A. J., and Canim A. J., "Selective vibration sensing: a new concept for activity-sensing rate-responsive pacing", PACE, vol. 11, September, 1988. Matula M., Schlegl M., and Alt E., "Activity controlled cardiac pacemakers during stairwalking: A comparison of accelerometer with vibration guided devices and with sinus rate", PACE, 1996, vol 19, 1036:1041. PA1 How do you feel? PA1 Are you as active today as you were 2 months ago? PA1 Are you as active today as you were 6 months ago? PA1 Are you able to climb stairs? PA1 How far can you walk? PA1 Do you do your own grocery shopping? PA1 Do you perform chores around the house? PA1 Are you able to complete your activities without resting? PA1 1. be sensitive enough to pick up low-level activities (activities of daily living) as well as high-level exertion activities, PA1 2. minimize response to activities such as automobile driving, PA1 3. be patient independent, i.e., it should not require a user programmable parameter and should PA1 4. be easy to implement in an implantable device
Specific Information Uses
The ability to perform normal daily activities is an important indicator of a patient's functional status and is related to improved quality of life in patients. An increase in the ability to perform activities of daily living (ADL) is an indicator of improving health and functional status, while a decrease in the ability to perform daily activities may be an important indicator of worsening health. Activities of daily living are submaximal activities performed during daily life. Examples are going to work, cleaning the house, vacuuming the house, cooking and cleaning, working in the garden, short walk to grocery stores, cleaning the car, and slow paced evening walks.
In order the assess the amount of daily activities that patients can perform and the ease with which they can perform these activities, clinicians typically ask their patients during office visits the following questions:
They also employ other tools such as the symptom based treadmill exercise test, the 6 minute walk test, questions and answers (Q&A), and quality of life(QOL) questionnaires in order to learn about their patients' ability to perform exercise and normal activities, but these assessment tools have limitations. Q&A techniques are subjective and biased towards recent events(at least partly due to patient bias toward present recall, if not also due to patient memory impairment or insufficiency, or a patient's desire to provide positive data). Maximal treadmill exercise tests assesses the patient's ability to perform intense (maximal) exercises and do not reflect the ability to perform normal daily activities. The 6 minute walk test has to be administered very carefully and rigorously to achieve valid results.
Impairment of functional status can be seen in changes in the ability to perform exercises and ADL. This can be affected by many physiological factors such as progressive decompensation in the setting of left ventricular cardiac (LV) dysfunction, beta blocker treatment, symptomatic arrhythmias, and depression. These changes may take place over a long period of time and may be too subtle to be discerned by patients.
Physicians use answers to these questions and observation in clinic to determine what New York Heart Association "class" into which a patient falls, and on this basis, among others, they administer and alter treatment. Class I is defined as "Patients with cardiac disease but without resulting limitation of physical activity. Ordinary physical activity does not cause undue fatigue, palpitation, dyspnea or anginal pain." NYHA Class II is "Patients with cardiac disease resulting in slight limitation of physical activity. They are comfortable at rest. Ordinary physical activity results in fatigue, palpitation, dyspnea or anginal pain.", Class III is defined: "Patients with marked limitation of physical activity. They are comfortable at rest. Less than ordinary activity causes fatigue, palpitation, dyspnea, or anginal pain. And, Class IV is "Patients with cardiac disease resulting in inability to carry on any physical activity without discomfort. Symptoms of heart failure or of the anginal syndrome may be present even at rest. If any physical activity is undertaken, discomfort is increased."
As implantable device technology advances, there is a further need to provide information that will allow the clinician to not only manage arrhythmias better but also the progression of other diseases (co-morbidities) that patients may have. With the advent of newer drugs and newer paradigms in drug therapy (the use of beta blockers in heart failure patients is just one example), there is a need for objective measures of patient response. Several parameters such as ventricular pressure, patient activity, lung wetness, and heart rate variability may provide such information to the clinician.
In the management of patient care over a relatively long period of time, it is believed that current implantable devices with their larger memories and even using some extant sensors may be enhanced to produce a set of data that indicates patient functional status on an on-going basis. For patients who do not have already a need for an implanted medical device as an adjunct to their medical therapy, the addition of a specialized implantable that has extremely limited capability and thus small size may provide an additional tool for medical management of disease, particularly Cardiac Heart Failure (CHF).
However, it seems that the simplest and possibly most accurate measurement which can determine the prognosis and progress of a patient has not been previously monitored, and further this indicator has not been monitored in a manner effective to elucidate for the physician the changing character of the patient's CHF disease progression.
If there were a simple and yet effective measure that could be reliably correlated with the progress of CHF, the use of other hemodynamic measures could be used to supplement it and could easily be added to an implantable device. This indication alone may provide a sufficient justification for implantation of a device. In other words, if a very inexpensive implantable device could be developed to chronically monitor a simple indicator of CHF prognosis, the care available to CHF patients could be improved by using this data. Administration of patient care based on this inexpensive implant's data would improve the lives of CHF patients by virtue of their needing less frequent clinic visits for drug titration and other observationally intense activities, since the status of the patient could be determined without resort to an expensive doctor visit by merely and viewing the data from these CHF status indicators, the drugs themselves could be adjusted, alarms could be sent, and other therapies automatically adjusted based on this status report. It is believed that the common usage of such systems awaits the development of an information resource such as is taught in this patent.
We have determined that a long term trend of physical activity in CHF patients may thus provide the clinician with an objective measure of the patient's life-style and functional status, and can be used in conjunction with other information, but as explained previously, an objective long term measure is currently unavailable from implantable medical devices. Having available a display of a trend of patient ability to perform ADL is useful in several situations.
Correlation of Physical Activity with Patient Testimony
Clinicians often encounter patients who find it difficult to verbalize their symptoms clearly. In such situations, an objective measure of patient activity stored in the device may help the clinician to decide an appropriate course of action. For example, if a patient complains vaguely of fatigue and shortness of breath and is not able to describe the limitations to his/her daily activities, a trend of activities may help the clinician. If the activity data could show a considerable decrease in patient activities, then the clinician may take the next step such as the evaluation of cardiac profile, pulmonary dysfunction, or existing drug therapy. On the other hand, if the long term trend of activity in this patient is consistently regular, (i.e., no decrease in patient activity), the clinician may take alternative steps to understand the difference between patient symptoms and device indicated activity data.
Clinicians may also encounter situations when patients are reluctant to discuss their symptoms. In situations such as these, a trend of activity data may help the clinician to question the patient or the patient's spouse and enable the patient to come forward with their symptoms.
Correlation of Physical Activity with Onset or Progression of Heart Failure
Heart Failure is a syndrome characterized by the coexistence of left ventricular dysfunction (low EF), arrhythmias, pulmonary and peripheral congestion, and symptoms of fatigue and shortness breath. A majority of ICD patients have low EF (&lt;40%) and decreased functional capacity (NYHA Class II, III and IV), and are at risk of developing heart failure. Clinical heart failure is a progressive disease; hence early identification and timely therapy may prevent hospitalizations, reduce the cost of care and improve patient lives.
In the earlier stages of heart failure, patients may not be able to perform strenuous activities and in the later stages, may not be able to perform even routine activities such as walking up a flight of stairs. Further, the inability to perform exercise and activities develop over a long period of time and hence may be difficult to discern and quantify. An objective measure of long term trends of patient activity may be useful in early identification of symptoms of heart failure and in the progression of heart failure. A gradual decrease in patient activities over the last 8 months may lead the clinician to suspect the development of heart failure. This may lead the clinician to take the next step in differential diagnosis.
The ability to perform daily activities is of particular relevance to the onset and progression of heart failure. Several studies in the literature have described the need for an objective measurement of activities of daily living in patients with heart failure. This is no doubt why the NYHA measures focus so much on this ability to perform daily activities.
Correlation of Physical Activity with Patient Response to Therapy
The use of beta-blockers in patients at risk of arrhythmias or heart failure is becoming common clinical practice. Beta-blocking agents are known to blunt the heart rate response, which may affect the patient's ability to perform certain activities. The optimal dosage of beta-blocking agents is difficult to predict and may require trial and error methods. Further, there is usually a 30-60 day time period immediately after initiation of drug therapy during which the patient may not be very active. Most patients acclimate to the therapy after this period, but some don't. By having an objective measurement of patient activities, the titration and adjustment to beta blockers and other drugs could be enhanced.
Correlation of Physical Activity with Arrhythmias
Arrhythmias may cause symptoms such as palpitations, fatigue, or presyncope. Some patients may spend a significant amount of time in arrhythmias such as atrial fibrillation and may not be able to perform daily activities. Such issues can be coordinated with measurements of activity for increased diagnostic value.
As is known in the art, implantable medical devices exist that have various accelerometers and piezocrystal activity sensors and the like which count the movement of the crystal or sensor with respect to a resting state. Medtronic brand implantable medical devices with piezoelectric crystal or accelerometer based activity sensors have the ability to convert a raw activity signal into the 2 second activity counts. In other words, the number of times the accelerometer or sensor moves in a two second period is called a 2 second activity count. However, nothing in the art describes a method or apparatus for succinctly and effectively compiling such data as activity counts to make that data effective to solve the problems in diagnosis and patient tracking described above.
What is needed is a device with a system to convert these activity counts or some equivalent of them into a measure of patient activity that is clinically meaningful. To review the specifics of the problem consider the raw signal. The raw activity signal and hence the processed activity counts is a result of vibrations due to body movement. Activities such as walking and running cause body movement and vibration; the faster or longer the walk, more the vibrations, and larger the activity signal. Even though the raw activity signal is a good measure of activities such as walking and running, these raw counts, without our invention do not provide an accurate assessment of patient physical exertion. This is for three classes of reasons.
1. Body Vibrations are not Always Proportional to Level of Exertion
Any activity that causes body movement such as walking and running will generate an activity signal. Studies have shown that the amplitude of the activity counts increases in a linear fashion for walking activities. However, this linear relationship between intensity of activity and the activity signal during walking does not apply to all activities. For example, walking up-stairs at the same speed will produce activity counts similar to walking on a flat surface even though the intensity of activity is higher while walking upstairs. Other examples are isometric exercise, stationary bike, and jogging in place. Since the activity counts are derived from this activity signal, they may not be an objective measure of patients' exertion levels for all types of activity. In fact even a simple activity such as walking may produce different activity counts depending on the human-ground interface, for example, walking on a carpeted surface versus an asphalt surface.
2. Lack of Specificity
Activities such as automobile driving that result in body vibrations but do not involve exertion may sometimes produce an activity signal that may be comparable in amplitude to the level of the activity signal during walking. Likewise, the orientation of an accelerometer may not pick up an activity like push ups, despite the large exertion.
3. Inter-person Variability
There is also inter-person variability for similar activities. Differing size and fat content of the patient body, as well as placement of the device in various locations and orientations will all contribute to this kind of variability.
Based on these considerations, it is clear that in order to convert the activity counts to a meaningful measure of patient activity, the system must